class_docstr: Return conditioned Behavior cloning agent.
docstr: Returns the agent's next action and update agent's state.
  
  Args:
    reward: float, the reward received from the agent's most recent action.
    observation: numpy array, the most recent observation.
  
  Returns:
    int, the selected action.
docstr_tok: ["Returns","the","agent","'s","next","action","and","update","agent","'s","state",".","Args",":","reward",":","float",",","the","reward","received","from","the","agent","'s","most","recent","action",".","observation",":","numpy","array",",","the","most","recent","observation",".","Returns",":","int",",","the","selected","action","."]
code: def step(self, reward, observation):
      """Returns the agent's next action and update agent's state.
  
      Args:
        reward: float, the reward received from the agent's most recent action.
        observation: numpy array, the most recent observation.
  
      Returns:
        int, the selected action.
      """
      self._record_observation(observation)
      return self._get_action()
  
imports: import functools
  from absl import logging
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.jax.agents.rainbow import rainbow_agent
  from dopamine.labs.offline_rl.jax import offline_rainbow_agent
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 6 for calling the JaxReturnConditionedBCAgent obj's underlying member method code with example usage: obj.step(reward, observation) and returns: self